The RL paradigm adoption for the CWB-RS
[1]}, [2]} is a natural solution to the above described issues of lack of a supervision signal, sequential aspects, and the interdependence between recommendations and users’ behaviors. The research on RL-based recommender systems is recently gaining attention in the community [3]}, [4]}, [5]} because of their flexibility,
and the growth of deep reinforcement learning field [5]}, [7]}. As suggested in [1]}, RL-based RSs allow solving not only the problem of frequent updates of the user profile, typical of RS in social media, and offer also a precise formulation of the initialization problem in terms of exploitation-exploration [9]}, [10]}.
